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Related Concept Videos

Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

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Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

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Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
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Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
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Pharmacokinetic Models: Overview01:20

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Pharmacokinetic models utilize mathematical analysis to achieve a detailed quantitative understanding of a drug's life cycle within the body. They are instrumental in simulating a drug's pharmacokinetic parameters, predicting drug concentrations over time, optimizing dosage regimens, linking concentrations with pharmacologic activity, and estimating potential toxicity.
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Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Updated: Nov 15, 2025

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion
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Designing self-assembling kinetics with differentiable statistical physics models.

Carl P Goodrich1,2, Ella M King3, Samuel S Schoenholz4

  • 1School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138; carl.goodrich@ist.ac.at.

Proceedings of the National Academy of Sciences of the United States of America
|March 3, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a method to design emergent kinetics by differentiating through physics models. This allows precise control over self-assembly processes and kinetic pathways in materials science and biotechnology.

Keywords:
colloidsinverse designself-assembly

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Area of Science:

  • Statistical physics
  • Biotechnology
  • Materials science

Background:

  • Designing component interactions for emergent structure is crucial.
  • Designing emergent kinetics has received less attention but is equally important.

Purpose of the Study:

  • To develop a method for precisely designing kinetic pathways using automatic differentiation.
  • To demonstrate control over emergent kinetics in self-assembly systems.

Main Methods:

  • Utilizing automatic differentiation with statistical physics models.
  • Performing free energy calculations and molecular dynamics simulations.
  • Manipulating particle interactions using gradient information.

Main Results:

  • Achieved precise design of kinetic pathways in bulk crystallization and nanocluster formation.
  • Tuned the rate of structure formation by manipulating inter-particle interactions.
  • Identified features in the design space for simultaneous and independent control of kinetic features.

Conclusions:

  • Provides a generalizable foundation for studying nonstructural self-assembly and emergent properties.
  • Enables precise control over kinetic properties in self-assembly systems.
  • Opens new avenues for designing complex emergent behaviors in various systems.